Shadow removal is an essential task in computer vision and computer graphics.
Recent shadow removal approaches all train convolutional neural networks (CNN)
on real paired shadow/shadow-free or shadow/shadow-free/mask image datasets.
However, obtaining a large-scale, diverse, and accurate dataset has been a big
challenge, and it limits the performance of the learned models on shadow images
with unseen shapes/intensities. To overcome this challenge, we present
SynShadow, a novel large-scale synthetic shadow/shadow-free/matte image
triplets dataset and a pipeline to synthesize it. We extend a
physically-grounded shadow illumination model and synthesize a shadow image
given an arbitrary combination of a shadow-free image, a matte image, and
shadow attenuation parameters. Owing to the diversity, quantity, and quality of
SynShadow, we demonstrate that shadow removal models trained on SynShadow
perform well in removing shadows with diverse shapes and intensities on some
challenging benchmarks. Furthermore, we show that merely fine-tuning from a
SynShadow-pre-trained model improves existing shadow detection and removal
models. Codes are publicly available at this https URL

介绍了 SynShadow，这是一个新的大规模合成阴影 / 无阴影 / 亚光图像三元组数据集及其产生方法：通过对物理接地阴影照明模型的扩展，利用透明图像、亚光图像和阴影衰减参数的任意组合来合成阴影图像，并展示了在一些具有挑战性的基准测试上，使用该数据集支持训练的阴影去除模型的优异表现以及简单微调自 SynShadow 预训练模型的改进效果，并且提供了公开的代码